CDS-MVSNet formulates a 3D cost volume based on the output features of CDSFNet. The features of CDSFNet effectively reduce the matching ambiguity by considering the proper pixel scales. Due to the robustness of our feature extraction and cost formulation, CDS-MVSNet can produce more accurate depths in coarser stages compared to other cascade MVS methods [1]}, [2]}, [3]}, [4]}. There is even little difference in the accuracy of the depths estimated at half resolution and full resolution. Therefore, differently from CasMVSNet, CDS-MVSNet only computes the depths of half-resolution images using three-step MVS computation and then upsamples the depths to the original resolution in the final stage. The upsampled depths at the original resolution can be refined by a 2D CNN to obtain the final depths. This approach can drastically reduce the computation time and GPU memory requirements.
